Structural health monitoring is becoming of ever-increasing significance for many industries. One industry for which this is becoming especially significant is the aerospace industry. This is because, among other things, the structural integrity of systems and components in the aerospace industry can possibly cause in-flight shutdowns, take-off aborts, delays, or cancellations, all of which can result in significant industry and consumer costs.
Some presently known structural health monitoring systems use arrays of various sensors. The use of such arrays, which can range from tens to hundreds of sensors, exhibits certain drawbacks. For example, installing each of the sensors one-by-one can be both labor-intensive and time-consuming When the sensors are implemented as phase arrays, which can be very sensitive to inaccuracies in the sensor placement, it may be necessary to assure the precise position of each of the sensors. Moreover, the sensor wiring can be relatively complicated, and the length, volume, and weight of the sensor wiring can be significant.
In addition to the above, various numerical methods have been developed to provide visual representations of damage maps for structural defect detection, localization, and sizing. Included among these known numerical methods are various computer tomography (CT) methods. The presently known CT methods can provide relatively precise defect images, but can also be relatively time-consuming and computationally intensive. Moreover, many CT methods rely on high density coverage of the monitored area. As a result, the CT methods may not be useful when real-time structural health monitoring is desired, because sparse sensor arrays are generally used for such applications.
Various numerical methods do exist for use with sparse sensor arrays. These methods are typically based on the detection of waves scattered by a defect, the use of a geometrical approach to spatial mapping of the scatters. For relatively complex structures, indentifying a wave reflected by a defect can be extremely challenging due to the presence of various structural elements such as stringers, stiffeners, borders, holes, rivets, bolts, etc., which can be sources of background reflections. One numerical method that has been developed that does not suffer from this drawback is known as the RAPID (Reconstruction Algorithm for Probabilistic Inspection of Defects) algorithm. The RAPID algorithm is based on the evaluation of signal differences, using a correlation analysis, between a baseline signal and actual signals in the direct path between sensor/actuator pairs. However, the RAPID algorithm exhibits several drawbacks of its own. For example, it is sensitive to phase synchronization between the baseline and actual signals. Moreover, images that are generated based on the RAPID algorithm can include false artifacts if certain parameters are not set optimally. These false artifacts may also be generated due to the non-uniform coverage provided by the network of direct paths between sensor/actuator pairs.
Hence, there is a need for a system and method for detecting, localizing, and evaluating the size of structural defects in real-time that does not exhibit the drawbacks noted above. Namely, a system and method that does not rely on the precise positioning of individual sensors and/or relatively complicated, long, voluminous, and heavy sensor wiring and/or is relatively insensitive to phase synchronization between the baseline and actual signals and/or does not generate images that include false artifacts if certain parameters are not set optimally. The present invention addresses one or more of these needs.